ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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COVID-19 pandemic and health worker stress: The mediating effect of emotional regulation
This article has 8 authors:Reviewed by ScreenIT
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Hospital-based autonomous pre-clinical screening of COVID-19: An emergency triage using a vital signs recording system, Paris-Ile de France region
This article has 5 authors:Reviewed by ScreenIT
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Simulation of COVID-19 Incubation Period and the Effect of Probability Distribution Function on Model Training Using MIMANSA
This article has 4 authors:Reviewed by ScreenIT
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Assessment of Clinical Characteristics and Mortality-Associated Factors in COVID-19 Critical Cases in Kuwait
This article has 6 authors:Reviewed by ScreenIT
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The use of denaturing solution as collection and transport media to improve SARS-CoV-2 RNA detection and reduce infection of laboratory personnel
This article has 16 authors:Reviewed by ScreenIT
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Antiviral treatment of SARS-CoV-2-infected hamsters reveals a weak effect of favipiravir and a complete lack of effect for hydroxychloroquine
This article has 28 authors:Reviewed by ScreenIT
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Phylogenetic pattern of SARS-CoV-2 from COVID-19 patients from Bosnia and Herzegovina: lessons learned to optimize future molecular and epidemiological approaches
This article has 16 authors:Reviewed by ScreenIT
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Dog Savior: Immediate Scent-Detection of SARS-COV-2 by Trained Dogs
This article has 15 authors:Reviewed by ScreenIT
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High-Density Amplicon Sequencing Identifies Community Spread and Ongoing Evolution of SARS-CoV-2 in the Southern United States
This article has 25 authors:Reviewed by ScreenIT
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SARS-CoV-2 Assays To Detect Functional Antibody Responses That Block ACE2 Recognition in Vaccinated Animals and Infected Patients
This article has 18 authors:Reviewed by ScreenIT